R Notebook

This file is to take the code from DeltaCals_Seasonality_CRD/YF85, DeltaCalcs_Seasonality_CRD/YF45 to make a panel plot for the seasonal change and decadal changes

Starting with the CRD

rm(list = ls())

library(ggplot2)
library(ggpubr)

This code is directly from the code files listed above - .csv files were made at the end of the “DeltaCalcs_Seasonality” files - those are loaded and used here

Reading in the data files

#2030
MonthlyStatsCRD45_2030 <- read.csv("Corr_MonthlyStatsCRD45_2030.csv") 
MonthlyStatsCRD85_2030 <- read.csv("Corr_MonthlyStatsCRD85_2030.csv") 

#2060
MonthlyStatsCRD45_2060 <- read.csv("Corr_MonthlyStatsCRD45_2060.csv") 
MonthlyStatsCRD85_2060 <- read.csv("Corr_MonthlyStatsCRD85_2060.csv") 

#2090
MonthlyStatsCRD45_2090 <- read.csv("Corr_MonthlyStatsCRD45_2090.csv") 
MonthlyStatsCRD85_2090 <- read.csv("Corr_MonthlyStatsCRD85_2090.csv") 

Creating the plots for 8.5

2030s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsCRD85_2030, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsCRD85_2030$Month <- as.numeric(MonthlyStatsCRD85_2030$Month)

# Plot using ggplot
MonthlyStatsCRD85_2030Plot <- ggplot(data = MonthlyStatsCRD85_2030, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  labs(x = "", y = "Delta Water Temperature (°C)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(axis.title = element_text(size = 16),
        axis.text = element_text(size = 14))
MonthlyStatsCRD85_2030Plot

2060s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsCRD85_2060, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsCRD85_2060$Month <- as.numeric(MonthlyStatsCRD85_2060$Month)

# Plot using ggplot
MonthlyStatsCRD85_2060Plot <- ggplot(data = MonthlyStatsCRD85_2060, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  labs(x = "", y = "") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(axis.title = element_text(size = 16),
        axis.text = element_text(size = 14))+
  theme(axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank())
MonthlyStatsCRD85_2060Plot

2090s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsCRD85_2090, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsCRD85_2090$Month <- as.numeric(MonthlyStatsCRD85_2090$Month)

# Plot using ggplot
MonthlyStatsCRD85_2090Plot <- ggplot(data = MonthlyStatsCRD85_2090, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(x = "", y = "") +
  theme(axis.title = element_text(size = 16),
        axis.text = element_text(size = 14))+
  theme(axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank())
MonthlyStatsCRD85_2090Plot

Panel Plot

ggarrange(MonthlyStatsCRD85_2030Plot, MonthlyStatsCRD85_2060Plot, MonthlyStatsCRD85_2090Plot, ncol = 3)

Creating the plots for 4.5

2030s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsCRD45_2030, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsCRD45_2030$Month <- as.numeric(MonthlyStatsCRD45_2030$Month)

# Plot using ggplot
MonthlyStatsCRD45_2030Plot <- ggplot(data = MonthlyStatsCRD45_2030, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  labs(x = "", y = "Delta Water Temperature (°C)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(axis.title = element_text(size = 16),
        axis.text = element_text(size = 14))
MonthlyStatsCRD45_2030Plot

2060s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsCRD45_2060, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsCRD45_2060$Month <- as.numeric(MonthlyStatsCRD45_2060$Month)

# Plot using ggplot
MonthlyStatsCRD45_2060Plot <- ggplot(data = MonthlyStatsCRD45_2060, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  labs(x = "", y = "") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(axis.title = element_text(size = 16),
        axis.text = element_text(size = 14))+
  theme(axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank())
MonthlyStatsCRD45_2060Plot

2090s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsCRD45_2090, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsCRD45_2090$Month <- as.numeric(MonthlyStatsCRD45_2090$Month)

# Plot using ggplot
MonthlyStatsCRD45_2090Plot <- ggplot(data = MonthlyStatsCRD45_2090, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(x = "", y = "") +
  theme(axis.title = element_text(size = 16),
        axis.text = element_text(size = 14))+
  theme(axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank())
MonthlyStatsCRD45_2090Plot

Panel Plot

ggarrange(MonthlyStatsCRD45_2030Plot, MonthlyStatsCRD45_2060Plot, MonthlyStatsCRD45_2090Plot, ncol = 3)

Creating plots with both RCP 4.5 and 8.5

2030s

MonthlyStatsCRD_2030Plot <- ggplot() +
  geom_line(data = MonthlyStatsCRD45_2030, aes(x = Month, y = mean_diff_AvgTemp, color = "4.5")) +
  geom_line(data = MonthlyStatsCRD85_2030, aes(x = Month, y = mean_diff_AvgTemp, color = "8.5")) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  geom_ribbon(data = MonthlyStatsCRD45_2030, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +
  geom_ribbon(data = MonthlyStatsCRD85_2030, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  ylim(-5, 15) +
  theme_classic() +
  labs(x = "", y = "Delta Water Temperature (°C)", color = "RCP") +  # Setting legend title
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title = element_text(size = 20),
        axis.text = element_text(size = 18)) +
  scale_color_manual(values = c("4.5" = "blue", "8.5" = "red"), labels = c("4.5", "8.5")) +  # Adjusting color scale
  theme(legend.position = "none")  # Removing the legend

MonthlyStatsCRD_2030Plot

2060s

MonthlyStatsCRD_2060Plot <- ggplot() +
  geom_line(data = MonthlyStatsCRD45_2060, aes(x = Month, y = mean_diff_AvgTemp, color = "4.5")) +
  geom_line(data = MonthlyStatsCRD85_2060, aes(x = Month, y = mean_diff_AvgTemp, color = "8.5")) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  geom_ribbon(data = MonthlyStatsCRD45_2060, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +
  geom_ribbon(data = MonthlyStatsCRD85_2060, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  ylim(-5, 15) +
  theme_classic() +
  labs(x = "", y = "", color = "RCP") + # Setting legend title
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title = element_text(size = 20),
        axis.text = element_text(size = 18))+
  theme(axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank()) +
  scale_color_manual(values = c("4.5" = "blue", "8.5" = "red"), labels = c("4.5", "8.5")) + # Adjusting color scale  
  theme(legend.position = "none")  # Removing the legend

MonthlyStatsCRD_2060Plot

2090s

MonthlyStatsCRD_2090Plot <- ggplot() +
  geom_line(data = MonthlyStatsCRD45_2090, aes(x = Month, y = mean_diff_AvgTemp, color = "4.5")) +
  geom_line(data = MonthlyStatsCRD85_2090, aes(x = Month, y = mean_diff_AvgTemp, color = "8.5")) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  geom_ribbon(data = MonthlyStatsCRD45_2090, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +
  geom_ribbon(data = MonthlyStatsCRD85_2090, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  ylim(-5, 15) +
  theme_classic() +
  labs(x = "", y = "", color = "RCP") + #setting the legend title
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 18))+
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank())+
  scale_color_manual(values = c("4.5" = "blue", "8.5" = "red"), labels = c("4.5", "8.5")) + # Adjusting color scale  
  theme(legend.position = "none")  # Removing the legend

MonthlyStatsCRD_2090Plot

Panel Plot

MonthlyStatsCRD <- ggarrange(MonthlyStatsCRD_2030Plot, MonthlyStatsCRD_2060Plot, MonthlyStatsCRD_2090Plot, ncol = 3)
MonthlyStatsCRD
ggsave("Corr_DeltaMonthCRD.png", plot = MonthlyStatsCRD, width = 12, height = 3)

Moving on to YF

This code is directly from the code files listed above - .csv files were made at the end of the “DeltaCalcs_Seasonality” files - those are loaded and used here

Reading in the data files

#2030
MonthlyStatsYF45_2030 <- read.csv("Corr_MonthlyStatsYF45_2030.csv") 
MonthlyStatsYF85_2030 <- read.csv("Corr_MonthlyStatsYF85_2030.csv") 

#2060
MonthlyStatsYF45_2060 <- read.csv("Corr_MonthlyStatsYF45_2060.csv") 
MonthlyStatsYF85_2060 <- read.csv("Corr_MonthlyStatsYF85_2060.csv") 

#2090
MonthlyStatsYF45_2090 <- read.csv("Corr_MonthlyStatsYF45_2090.csv") 
MonthlyStatsYF85_2090 <- read.csv("Corr_MonthlyStatsYF85_2090.csv") 

Creating the plots for 8.5

2030s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsYF85_2030, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsYF85_2030$Month <- as.numeric(MonthlyStatsYF85_2030$Month)

# Plot using ggplot
MonthlyStatsYF85_2030Plot <- ggplot(data = MonthlyStatsYF85_2030, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  labs(x = "", y = "Delta Water Temperature (°C)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 18))
MonthlyStatsYF85_2030Plot

2060s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsYF85_2060, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsYF85_2060$Month <- as.numeric(MonthlyStatsYF85_2060$Month)

# Plot using ggplot
MonthlyStatsYF85_2060Plot <- ggplot(data = MonthlyStatsYF85_2060, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  labs(x = "", y = "") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 18))+
  theme(axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank())
MonthlyStatsYF85_2060Plot

2090s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsYF85_2090, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsYF85_2090$Month <- as.numeric(MonthlyStatsYF85_2090$Month)

# Plot using ggplot
MonthlyStatsYF85_2090Plot <- ggplot(data = MonthlyStatsYF85_2090, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(x = "", y = "") +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 18))+
  theme(axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank())
MonthlyStatsYF85_2090Plot

Panel Plot

ggarrange(MonthlyStatsYF85_2030Plot, MonthlyStatsYF85_2060Plot, MonthlyStatsYF85_2090Plot, ncol = 3)

Creating the plots for 4.5

2030s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsYF45_2030, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsYF45_2030$Month <- as.numeric(MonthlyStatsYF45_2030$Month)

# Plot using ggplot
MonthlyStatsYF45_2030Plot <- ggplot(data = MonthlyStatsYF45_2030, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  labs(x = "", y = "Delta Water Temperature (°C)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 18))
MonthlyStatsYF45_2030Plot

2060s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsYF45_2060, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsYF45_2060$Month <- as.numeric(MonthlyStatsYF45_2060$Month)

# Plot using ggplot
MonthlyStatsYF45_2060Plot <- ggplot(data = MonthlyStatsYF45_2060, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  labs(x = "", y = "") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 18))+
  theme(axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank())
MonthlyStatsYF45_2060Plot

2090s

# 2. Monthly averages across ponds
ggplot(data = MonthlyStatsYF45_2090, aes(x = Month)) +
  geom_point(aes(y = mean_diff_AvgTemp)) +
  theme_classic()

# Convert Month from character to numeric to ensure proper ordering in ggplot
MonthlyStatsYF45_2090$Month <- as.numeric(MonthlyStatsYF45_2090$Month)

# Plot using ggplot
MonthlyStatsYF45_2090Plot <- ggplot(data = MonthlyStatsYF45_2090, aes(x = Month)) +
  geom_line(aes(y = mean_diff_AvgTemp)) +  # Point plot with mean values
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +  # Horizontal dashed line at y = 0
  geom_ribbon(aes(ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +  # Ribbon for positive and negative error based on sd values
  scale_x_continuous(breaks = 1:12, labels = month.abb) +  # Adjust x-axis labels to show month abbreviations
  ylim(-5,15)+
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(x = "", y = "") +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 18))+
  theme(axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank())
MonthlyStatsYF45_2090Plot

Panel Plot

ggarrange(MonthlyStatsYF45_2030Plot, MonthlyStatsYF45_2060Plot, MonthlyStatsYF45_2090Plot, ncol = 3)

Creating plots with both RCP 4.5 and 8.5

2030s

MonthlyStatsYF_2030Plot <- ggplot() +
  geom_line(data = MonthlyStatsYF45_2030, aes(x = Month, y = mean_diff_AvgTemp, color = "4.5")) +
  geom_line(data = MonthlyStatsYF85_2030, aes(x = Month, y = mean_diff_AvgTemp, color = "8.5")) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  geom_ribbon(data = MonthlyStatsYF45_2030, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +
  geom_ribbon(data = MonthlyStatsYF85_2030, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  ylim(-5, 15) +
  theme_classic() +
  labs(x = "", y = "Delta Water Temperature (°C)", color = "RCP") +  # Setting legend title
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title = element_text(size = 20),
        axis.text = element_text(size = 18)) +
  scale_color_manual(values = c("4.5" = "blue", "8.5" = "red"), labels = c("4.5", "8.5")) +  # Adjusting color scale
  theme(legend.position = "none")  # Removing the legend

MonthlyStatsYF_2030Plot

2060s

MonthlyStatsYF_2060Plot <- ggplot() +
  geom_line(data = MonthlyStatsYF45_2060, aes(x = Month, y = mean_diff_AvgTemp, color = "4.5")) +
  geom_line(data = MonthlyStatsYF85_2060, aes(x = Month, y = mean_diff_AvgTemp, color = "8.5")) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  geom_ribbon(data = MonthlyStatsYF45_2060, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +
  geom_ribbon(data = MonthlyStatsYF85_2060, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  ylim(-5, 15) +
  theme_classic() +
  labs(x = "", y = "", color = "RCP") + # Setting legend title
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title = element_text(size = 20),
        axis.text = element_text(size = 18))+
  theme(axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank()) +
  scale_color_manual(values = c("4.5" = "blue", "8.5" = "red"), labels = c("4.5", "8.5")) + # Adjusting color scale  
  theme(legend.position = "none")  # Removing the legend

MonthlyStatsYF_2060Plot

2090s

MonthlyStatsYF_2090Plot <- ggplot() +
  geom_line(data = MonthlyStatsYF45_2090, aes(x = Month, y = mean_diff_AvgTemp, color = "4.5")) +
  geom_line(data = MonthlyStatsYF85_2090, aes(x = Month, y = mean_diff_AvgTemp, color = "8.5")) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  geom_ribbon(data = MonthlyStatsYF45_2090, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "blue", alpha = 0.3) +
  geom_ribbon(data = MonthlyStatsYF85_2090, aes(x = Month, ymin = mean_diff_AvgTemp - sd_diff_AvgTemp, ymax = mean_diff_AvgTemp + sd_diff_AvgTemp), fill = "red", alpha = 0.3) +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  ylim(-5, 15) +
  theme_classic() +
  labs(x = "", y = "", color = "RCP") + #setting the legend title
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 18))+
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.text.y = element_blank(),  # Remove Y-axis tick mark labels
        axis.ticks.y = element_blank())+
  scale_color_manual(values = c("4.5" = "blue", "8.5" = "red"), labels = c("4.5", "8.5")) + # Adjusting color scale  
  theme(legend.position = "none")  # Removing the legend

MonthlyStatsYF_2090Plot

Panel Plot

# Combine plots into a panel with one legend
library(ggpubr)
MonthlyStatsYF <- ggarrange(
  MonthlyStatsYF_2030Plot,
  MonthlyStatsYF_2060Plot,
  MonthlyStatsYF_2090Plot,
  ncol = 3,
  common.legend = TRUE,  # Ensure common legend
  legend = "bottom"  # Position the legend to the right of the panel
)

# Save the combined plo
ggsave("Corr_DeltaMonthYF.png", plot = MonthlyStatsYF, width = 12, height = 4)

Creating one giant panel plot

library(ggplot2)
library(ggpubr)

# Define a function to remove the legend
remove_legend <- function(plot) {
  plot + theme(legend.position = "none")
}

# Modify the MonthlyStatsYF_2060Plot with a customized legend title and position
MonthlyStatsYF_2060PlotWithLegend <- MonthlyStatsYF_2060Plot +
  guides(color = guide_legend(
    title = NULL,
    override.aes = list(size = 2) # Increase the line size in the legend
  )) +
  theme(
    legend.position = c(0.95, 0.98), # Adjust x and y coordinates for position (farther up)
    legend.justification = c(1, 1), # Adjust justification to position the legend correctly
    legend.text = element_text(size = 24) # Increase the legend text size (3 times the default size)
  )
# Combine all plots
combined_plot <- ggarrange(
  remove_legend(MonthlyStatsCRD_2030Plot),
  remove_legend(MonthlyStatsCRD_2060Plot),
  remove_legend(MonthlyStatsCRD_2090Plot),
  remove_legend(MonthlyStatsYF_2030Plot),
  MonthlyStatsYF_2060PlotWithLegend, # Plot with the legend
  remove_legend(MonthlyStatsYF_2090Plot),
  ncol = 3, nrow = 2,
  common.legend = FALSE
)

combined_plot

# Save the combined plot
ggsave("Corr_DeltaMonth_Combined.png", combined_plot, width = 15, height = 10)